Rotationally Invariant Crystallographic Representation for Enhancing the Inverse Design of Materials

Abstract The inverse design of materials represents a pivotal technology in the domains of materials science and information engineering. It facilitates the prediction of material properties and structures from desired functionality, thereby accelerating the development of new materials. Nevertheless, conventional material design methodologies frequently necessitate the utilization of extensive computational simulations and experiments, which are inherently time‐consuming and resource‐intensive. A novel approach to materials inverse design is put forth, namely a rotationally invariant crystallographic representation (RICR) coupled with a variational autoencoder (VAE) generative model. The RICR effectively encapsulates the salient features of the crystal structure while preserving rotational invariance through the incorporation of rotational coordinate features. By conducting inverse design experiments on data from the Materials Project database, new crystal materials with user‐specified band gaps and formation energies are successfully design and validated. The results demonstrate that RICR achieves improvements in reconstruction accuracy, performance in mapping regression branch attributes, and the success rate of generating target crystal designs. These findings indicate the effectiveness of RICR in inverse materials design and suggest that this method can provide robust theoretical support for inverse design across diverse materials, thereby advancing the development and innovation of materials science.